Team Project Proposal

Visualising the climate change over time

For this document, the following packages are required:

library(tidyverse)
library(knitr)
library(zoo)

Original Data Visualization in Climate Change

The ongoing dialogue surrounding climate change has increasingly focused on the empirical data that underscores global temperature trends. NOAA’s recent visualization of global temperature anomalies from 1880 to 2023 offers a comprehensive view of the planet’s warming trajectory. This project aims to elucidate the correlation between anthropogenic activities and climate change, highlighting how industrialization and carbon emissions have influenced global temperatures.

The visualization spans over a century of data, capturing periods of significant climatic shifts. Despite its clarity in depicting long-term trends, the inclusion of interactive features (as illustrated in Figure 1) enhances user engagement with the data. However, there is potential for further refinement. Incorporating more dynamic elements such as temporal sliders, geospatial overlays, and chloropleth map integration could offer a more nuanced understanding of how specific policies and global events impact climate change on a regional and global scale.

Figure 1: Global Land and Ocean

Critical Assessment of the Original Visualization

Proposed Improvements

Choropleth Map Integration

Data Cleaning

# Load the data
data <- read_csv("data/country_temp.csv")

# Convert the date column to Date format and extract the year and month
data$dt <- as.Date(data$dt, format = "%Y-%m-%d")
data$year <- as.numeric(format(data$dt, "%Y"))
data$month <- format(data$dt, "%m")

# Group by country, year, and month, then calculate the average temperatures rounded to 2 decimal points
monthly_avg_all_countries_initial <- data |>
  group_by(Country, year, month) |>
  summarize(monthly_avg = round(mean(AverageTemperature, na.rm = TRUE), 2))

# Display the tail of the initial data frame
knitr::kable(tail(monthly_avg_all_countries_initial), caption = "Tail of Monthly Average Temperatures by Country", digits = 2)

Tail of Monthly Average Temperatures by Country

Country	year	month	monthly_avg
Åland	2013	04	1.70
Åland	2013	05	10.33
Åland	2013	06	14.07
Åland	2013	07	16.45
Åland	2013	08	16.42
Åland	2013	09	NaN

# Filter the data for Afghanistan in the year 1839
afghanistan_1839_initial <- monthly_avg_all_countries_initial |>
  filter(Country == "Afghanistan" & year == 1839)

# Display the filtered data
print(afghanistan_1839_initial)

# A tibble: 12 × 4
# Groups:   Country, year [1]
   Country      year month monthly_avg
   <chr>       <dbl> <chr>       <dbl>
 1 Afghanistan  1839 01            NaN
 2 Afghanistan  1839 02            NaN
 3 Afghanistan  1839 03            NaN
 4 Afghanistan  1839 04            NaN
 5 Afghanistan  1839 05            NaN
 6 Afghanistan  1839 06            NaN
 7 Afghanistan  1839 07            NaN
 8 Afghanistan  1839 08            NaN
 9 Afghanistan  1839 09            NaN
10 Afghanistan  1839 10            NaN
11 Afghanistan  1839 11            NaN
12 Afghanistan  1839 12            NaN

# Check for NA and NaN values in the monthly_avg_all_countries_initial dataframe
nan_check_initial <- sapply(monthly_avg_all_countries_initial, function(x) sum(is.na(x)))
print(nan_check_initial)

    Country        year       month monthly_avg 
          0           0           0       32651 

# Display rows with NA and NaN values
rows_with_nan_initial <- monthly_avg_all_countries_initial[apply(monthly_avg_all_countries_initial, 1, function(row) any(is.na(row))), ]
print(rows_with_nan_initial)

# A tibble: 32,651 × 4
# Groups:   Country, year [3,860]
   Country      year month monthly_avg
   <chr>       <dbl> <chr>       <dbl>
 1 Afghanistan  1838 05            NaN
 2 Afghanistan  1838 12            NaN
 3 Afghanistan  1839 01            NaN
 4 Afghanistan  1839 02            NaN
 5 Afghanistan  1839 03            NaN
 6 Afghanistan  1839 04            NaN
 7 Afghanistan  1839 05            NaN
 8 Afghanistan  1839 06            NaN
 9 Afghanistan  1839 07            NaN
10 Afghanistan  1839 08            NaN
# ℹ 32,641 more rows

# Remove rows with "Antarctica" from the dataset as the entire dataset for it is NaN
monthly_avg_all_countries_initial <- monthly_avg_all_countries_initial |> filter(Country != "Antarctica")

# Verify that "Antarctica" rows are removed
print(monthly_avg_all_countries_initial |> filter(Country == "Antarctica"))

# A tibble: 0 × 4
# Groups:   Country, year [0]
# ℹ 4 variables: Country <chr>, year <dbl>, month <chr>, monthly_avg <dbl>

# Function to fill NaN values using zoo library
# It is used for filling missing values through linear interpolation and forward/backward filling, ensuring continuous and complete temperature records.
fill_nan <- function(df) {
  df |>
    group_by(Country) |>
    mutate(monthly_avg = na.approx(monthly_avg, na.rm = FALSE, rule = 2)) |>
    mutate(monthly_avg = ifelse(is.na(monthly_avg), zoo::na.locf(monthly_avg, na.rm = FALSE), monthly_avg)) |>
    mutate(monthly_avg = ifelse(is.na(monthly_avg), zoo::na.locf(monthly_avg, fromLast = TRUE, na.rm = FALSE), monthly_avg)) |>
    ungroup()
}

# Apply fill_nan function
monthly_avg_all_countries_filled <- fill_nan(monthly_avg_all_countries_initial)

# Function to fill remaining NaN values with median of 5-year window for the same month
fill_with_window_month_median <- function(df) {
  df |>
    group_by(Country, month) |>
    mutate(monthly_avg = ifelse(is.na(monthly_avg), 
                                sapply(seq_along(monthly_avg), function(i) {
                                  if (is.na(monthly_avg[i])) {
                                    start_year <- year[i] - 5
                                    end_year <- year[i] + 5
                                    window_median <- median(df$monthly_avg[df$Country == Country[i] & df$month == month[i] & df$year >= start_year &df$year <= end_year & !is.na(df$monthly_avg)], na.rm = TRUE)
                                    return(window_median)
                                  } else {
                                    return(monthly_avg[i])
                                  }
                                }), 
                                monthly_avg)) |>
    ungroup()
}

# Apply window month median filling function
monthly_avg_all_countries_median <- fill_with_window_month_median(monthly_avg_all_countries_filled)

# Round the final dataframe to 2 decimal places
monthly_avg_all_countries_median <- monthly_avg_all_countries_median |>
  mutate(monthly_avg = round(monthly_avg, 2))

# Checking for remaining NaN values in the median imputed dataset
nan_check_median <- sapply(monthly_avg_all_countries_median, function(x) sum(is.na(x)))
print(nan_check_median)

    Country        year       month monthly_avg 
          0           0           0           0 

# Display rows with NaN values in the median imputed dataset
rows_with_nan_median <- monthly_avg_all_countries_median[apply(monthly_avg_all_countries_median, 1, function(row) any(is.na(row))), ]
print(rows_with_nan_median)

# A tibble: 0 × 4
# ℹ 4 variables: Country <chr>, year <dbl>, month <chr>, monthly_avg <dbl>

# Filter the data for Afghanistan in the year 1839
afghanistan_1839_median <- monthly_avg_all_countries_median |>
  filter(Country == "Afghanistan" & year == 1839)

# Display the filtered data
print(afghanistan_1839_median)

# A tibble: 12 × 4
   Country      year month monthly_avg
   <chr>       <dbl> <chr>       <dbl>
 1 Afghanistan  1839 01           6.51
 2 Afghanistan  1839 02           6.03
 3 Afghanistan  1839 03           5.55
 4 Afghanistan  1839 04           5.07
 5 Afghanistan  1839 05           4.59
 6 Afghanistan  1839 06           4.11
 7 Afghanistan  1839 07           3.62
 8 Afghanistan  1839 08           3.14
 9 Afghanistan  1839 09           2.66
10 Afghanistan  1839 10           2.18
11 Afghanistan  1839 11           1.7 
12 Afghanistan  1839 12           1.22

Joining with Country Codes

country_codes <- read_csv("data/country_codes.csv")

country_codes$Country <- gsub("\\s*\\([^)]*\\)|\\s*\\[[^]]*\\]|\\s+Islands", "", country_codes$Country)

country_codes$Country <- gsub("Russian Federation", "Russia", country_codes$Country)
country_codes$Country <- gsub("United States of America", "United States", country_codes$Country)
country_codes$Country <- gsub("United Kingdom of Great Britain and Northern Ireland", "United Kingdom", country_codes$Country)
country_codes$Country <- gsub("Viet Nam", "Vietnam", country_codes$Country)
country_codes$Country <- gsub("Bonaire, Sint Eustatius and Saba", "Bonaire, Saint Eustatius And Saba", country_codes$Country)
country_codes$Country <- gsub("Myanmar", "Burma", country_codes$Country)
country_codes$Country <- gsub("Cabo Verde", "Cape Verde", country_codes$Country)
country_codes$Country <- gsub("Czechia", "Czech Republic", country_codes$Country)
country_codes$Country <- gsub("Côte d'Ivoire", "Côte D'Ivoire", country_codes$Country)
country_codes$Country <- gsub("Micronesia", "Federated States Of Micronesia", country_codes$Country)
country_codes$Country <- gsub("French Southern Territories", "French Southern And Antarctic Lands", country_codes$Country)
country_codes$Country <- gsub("Guinea-Bissau", "Guinea Bissau", country_codes$Country)
country_codes$Country <- gsub("Heard Island and McDonald", "Heard Island And Mcdonald", country_codes$Country)
country_codes$Country <- gsub("Isle of Man", "Isle Of Man", country_codes$Country)
country_codes$Country <- gsub("Macao", "Macau", country_codes$Country)
country_codes$Country <- gsub("Republic of North Macedonia", "Macedonia", country_codes$Country)
country_codes$Country <- gsub("Palestine, State of", "Palestina", country_codes$Country)

country_codes$Country <- gsub("Saint Vincent and The Grenadines", "Saint Vincent And The Grenadines", country_codes$Country)
country_codes$Country <- gsub("South Georgia And The South Sandwich", "South Georgia And The South Sandwich Isla", country_codes$Country)
country_codes$Country <- gsub("Syrian Arab Republic", "Syria", country_codes$Country)
country_codes$Country <- gsub("Tanzania, United Republic of", "Tanzania", country_codes$Country)
country_codes$Country <- gsub("Timor-Leste", "Timor Leste", country_codes$Country)
country_codes$Country <- gsub(" and ", " And ", country_codes$Country)


monthly_avg_all_countries_median$Country <- gsub("\\s*\\([^)]*\\)|\\s*\\[[^]]*\\]|\\s+Islands", "",monthly_avg_all_countries_median$Country)


joinedDT <-
  full_join(
    monthly_avg_all_countries_median,
    country_codes,
    by = c("Country")
  )


missing_data <- joinedDT %>% filter(is.na(joinedDT$`Alpha2`))

unique_countries <- missing_data %>%
  distinct(Country, .keep_all = TRUE)

# Print the unique countries
# Display the tail of the initial data frame
knitr::kable(unique_countries, caption = "List of Unique Countries with no Country Codes")

List of Unique Countries with no Country Codes

Country	year	month	monthly_avg	Alpha2	Alpha3	Numeric
Africa	1850	01	19.76	NA	NA	NA
Asia	1816	04	6.98	NA	NA	NA
Baker Island	1825	01	25.58	NA	NA	NA
British Virgin	1824	01	24.79	NA	NA	NA
Europe	1743	11	3.94	NA	NA	NA
Gaza Strip	1808	10	21.31	NA	NA	NA
Kingman Reef	1883	01	25.65	NA	NA	NA
Laos	1816	03	21.65	NA	NA	NA
Namibia	1857	01	23.05	NA	NAM	516
North America	1768	09	9.76	NA	NA	NA
Oceania	1852	07	14.44	NA	NA	NA
Palmyra Atoll	1883	01	25.66	NA	NA	NA
Reunion	1787	01	26.11	NA	NA	NA
Saint Vincent And The Grenadines	1824	01	25.55	NA	NA	NA
South America	1851	01	23.64	NA	NA	NA
South Georgia And The South Sandwich Isla	1874	12	2.94	NA	NA	NA
Swaziland	1857	01	21.63	NA	NA	NA
Turks And Caicas	1823	01	23.82	NA	NA	NA

countries_cleaned <- joinedDT[!is.na(joinedDT$Alpha3), ]

# Check the cleaned data
library(DT)
datatable(countries_cleaned, caption = "Cleaned Data Joined with Country Codes")

Conclusion

The data is now ready for visualization. The next step will be to create a plot that can effectively communicate the relationship between global temperature anomalies and anthropogenic activities over time, and additionally allow curious readers to explore the data even further using interactivity. We will use Plotly to create the plot and add interactivity.

References

To improve on

Country Codes

Ideas